ACM SIGGRAPH 2006 Sketches
Piecewise affine kernel tracking for non-planar targets
Pattern Recognition
Interactive Tracking of 2D Generic Objects with Spacetime Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Probabilistic fusion-based parameter estimation for visual tracking
Computer Vision and Image Understanding
Inverse composition for multi-kernel tracking
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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This paper introduces a novel kernel-based method for template tracking in video sequences. The method is derived for a general warping transformation, and its application to affine motion tracking is further explored. Our approach is based on maximization of the multi-kernel Bhattacharyya coefficient with respect to the warp parameters. We explicitly compute the gradient of the similarity functional, and use a quasi-Newton procedure for optimization. Additionally, we consider a simple extension of the method that employs an illumination model correction to allow tracking under varying lighting conditions. The resulting tracking procedure is evaluated on a number of examples including large templates tracking non-rigidly moving textured areas.